Even with this, the sizes of the applications have increased, and they need optimally allocated memory to run. The requirement from memory management is always to keep memory available for the currently running processes. The following are the reasons we need memory management. When we generally work on a multiprogramming system, several processes are running in the background. To solve this, the memory manager takes care of the executed and to be executed processes and allocates and frees up memory accordingly, making the execution of processes smooth and memory efficient.
With the execution of multiple processes, one process may write in the address space of another process. This is why every process must be protected against unwanted interference by any other process. The memory manager, in this situation, protects the address space of every single process.
Keeping in mind the relocation algorithm too. The protection aspect and the relocation aspect of the memory manager work in synchronization. When multiple processes run in the main memory, it is required to have a protection mechanism that must allow several processes to access the same portion of the main memory. Allowing each process the access to the same memory or the identical copy of a program rather than having a copy for each program has an advantage of efficient memory allocation.
Memory management allows controlled access to the shared memory without compromising the protection. The memory management allows the allocation, use, and access of memory to the user programs in a manner that does not make chaos by modifying some file which was not supposed to be accessed by the user.
It supports a basic module that provides the required protection and sharing. The management modules are written and compiled independently so that all the references must be resolved by the system at run time.
It provides different modules with different degrees of protection and also supports sharing based on the user specification. The structure of the memory consists of the volatile main memory and secondary non-volatile memory. Applications are stored in the secondary memory, which is the hard drive of your computer. But when you run an application, it moves to the main memory, the RAM of the system.
To maintain the flow of these transfers from the main memory to the secondary memory with ease, proper management of memory is required. For better utilization of memory and flow of execution, we divide the memory into different sections to be used by the resident programs. The process of dividing the memory into sections is called memory partitioning. There are different ways in which memory can be partitioned:. In fixed partitioning, the number of non-overlapping partitions in RAM is fixed, but the size of each partition may not be the same.
As the allocation of memory is contiguous, no spanning is allowed. In fixed partitioning, the partitions are made either before execution or during system configuration.
In dynamic partitioning, the primary memory is emptied, and partitions are made during the run time according to the needs of the different processes.
RAM assessment methods have also been successfully implemented for humans TBI, also referred to as head injury or intracranial injury, is an epidemic that arises from brain damage caused by an external force. Approximately 10 million people suffer from TBI each year Tests entail an eight-arm radial maze. Short-term and long-term memory are evaluated after a maze test. Short-term memory, also referred to as the working memory, is the transient memory of task-related information in competitive environments By contrast, long-term memory, also referred to as the reference memory, is the permanent memory developed in natural adaptation to repetitive stimulation by the processing of the same information In addition to the short-term and long-term memory, other quantities related to search behaviors merit further investigation.
Although search trajectories in mazes can serve as an indicator, they are mostly used in the studies related to water mazes 19 , 20 , while hardly used in RAM-based studies. Some water maze-based studies have suggested that animals with variant pathologies adopt different search trajectories.
For example, rats with hippocampal lesions circled around their targets, whereas those with global cerebral ischemia wandered around their targets However, few studies have been conducted on the search trajectories of animals due to a poor repeatability 21 in a water maze test, simply because rats escape by swimming under an extremely high level of stress.
Therefore, a water maze is not a good choice to objectively assess the memory of animals with motor function impairment. Stress has been acknowledged as a confounding factor to the accuracy of water maze experiments. Instead, there is a higher reproducibility in RAM tests, since test subjects experience relatively low psychological stress when food is used as an incentive in tests.
Thus, an RAM is used to investigate the food search trajectory of rats with cognitive impairment. In the former case, tracking data were provided using sensors affixed to an animal 22 , 23 , while positions were evaluated in the latter case 24 , However, as will be seen below, each tracking strategy has its own advantage and disadvantage.
Background subtraction strategy works well on the condition that the background is static while the animals are mobile 29 , 30 , 31 , and remains one of the commonest computer vision techniques for object detection.
It can handle complex backgrounds and take care of non-uniform illumination problems. However, it fails when the tracked animals remain static for a long period of time, or if the illumination varies over time. Adaptive thresholding technique is a simple computer vision technique used to segment an object of interest from its background by binarizing an image Using the adaptive thresholding technique skillfully, animals can be reliably located from non-uniformly illuminated or non-static backgrounds, e.
Use of a Kalman filter had been validated as an efficient way to deal with tracking issues 25 , A wide variety of tracking algorithms have been proposed as well, e. Recently, deep learning models have been well applied to target tracking issues 35 , 36 , 37 , including automatic detection of marine species in aerial imagery 38 and identification of individual animals in a crowd Many high-performance algorithms employ convolutional neural networks CNNs as feature extractors. A large number of tracking algorithms have been proposed for different purposes.
Open-source tracking software is released to the general public, while nobody but programmers can modify the codes to meet their specific requirements. For this sake, commercial software is the No. Despite a licensed copy of SMART video tracking system 42 available in our laboratory, our team decided to develop our own tracking algorithm for the following reason.
The droppings of a rat are liable to be misidentified as a forever nonmoving target by the SMART video tracking system, and the worst case scenario is that an experiment goes endless regardless of the rat searching for food.
As a straightforward, but neither elegant nor efficient, solution, droppings are mopped up manually and instantly so as to keep the image recognition algorithm working. It would take an experimenter hours to watch all the rats go through experiments.
In a light room, the droppings of rats reflect light, and hence become noise sources to signal processing. The noise levels could be lowered in a dimly lit room, and all the experiments herein were performed in darkness accordingly.
Here is another reason for our team to develop this work. In the 2-week training program prior to a radial arm maze test, rats were pre-trained to locate the food in 4 out of 8 arms. Hence, an experimenter must be alerted to guide the rat to move on to the next baited arm s.
However, the intrusive experimenter is definitely a high-level noise source to the position tracking algorithm. In other words, a rat must be located in real time for efficient training, and a high robustness against an intruder is also required to avoid misidentification. As will be seen below, the presented position tracking algorithm was designed in such a way that both requirements can be fulfilled.
This work aimed to develop a real-time tracking algorithm with a high robustness against an intruder by which the food search trajectories of rats can be monitored and then analyzed. An infrared night-vision camera model: TP-Link C was placed 2. Experiments were conducted in a dimly lit room, where the camera transmitted real-time images wirelessly to a computer in another room for statistical analysis.
An experimenter watched the food searching progress in real time through a user interface UI. The computer beeped when the searching job was done. Thus, there was no need to the experimenter to remain on standby over the course of an experiment. The codes were written using Python. As illustrated in Fig. A rat was located as follows. In this work, an animal image was tracked using the OpenCV library First, an IR image i.
The gray level at pixel i, j is given by. Accordingly, gray levels are binarized with a threshold of The binary image was duplicated as Images 1 and 2 in Fig. An opening operation of an image A by the structuring element B in Eq. Then, the white outline and the coordinates of the center were saved in Array 1. Subsequently, the white outline and the coordinates of the center were saved in Array 2. If multiple white outlines were contained in Array 3, it means that there were noise spots in the images.
Here is an example. As depicted in Fig. The binary image showed white spots and strips caused by the rat and the shoe sole. Most noise spots appeared outside the RAM, and were removed after an opening operation. A Position tracking procedure for rats, B removal of noise spots caused by an experimenter crossing an arm, and C by the experimenter whose body covered part of the RAM.
Here is another example to illustrate the noise spot removal process. The coordinates of the center of the rat was saved in a hard drive every 0. As illustrated in the upper half of Fig. A rat was released in the central platform, and an experiment started. The distance between the proximal end of an arm i.
If the distance was less than a threshold, the rat was seen as having entered into arm. Subsequently, another variable AF[i] was used to indicate whether an arm was baited or not. AF stands for Arm Food, and the argument i ranges between 0 and 7. If this distance was less than a threshold, the rat was seen as having exited the arm.
In this scenario, MazeState was reset to 0. Therefore, short-term memory error was added by 1. Figure 8 illustrates the UI of the developed system. Here is how it works. In Frame 5, the location of the rat was displayed in real time.
The long-term, short-term memory error and the latency were presented in Frame 8, and quantities such as the sequence of accessed arms were shown in Frame 9. After a test was completed, the system automatically terminated, saved all the quantities as an excel file in Fig. The percussion force generated was 1. Prior to the experiment, the rats were completely anesthetized by introducing a Zoletil mixture into their bodies through intramuscular injection.
Next, the rats were tethered to a Kopf stereotaxic instrument, where their heads were fixated by placing an iron rod in each of their two ear sockets. The point 3. The injection cap was connected to the fluid percussion device to induce percussion. For the Sham group, sham surgeries were performed and no percussion force was applied.
On Days 1 and 2, the rats were introduced to the maze. No food was placed in any arm to allow the rats to familiarize themselves with the new environment.
On Days 3—5, food was placed in the distal end of each arm so as to guide the rats to reach each arm. On Days 8—12, food was placed in the distal end of four arms, and trained the rats to memorize the locations of the four baited arms. A day before the induction of TBI, a presurgery test was conducted in the maze, and the rats took a week of rest afterwards.
Then, each rat was subjected to a maze test on a weekly basis with the same baited arms as before. The tests lasted for 1 month.
All the methods proposed here were performed in accordance with relevant institutional guidelines and regulations. The statements on Ethics approval and consent to participate in the study are reported in the Methods—Experimental animals section.
This article aimed to compare the food searching performance, including the search trajectory, between the Sham and the TBI group. However, it was observed that a rat did not keep searching for food in an RAM. For example, as presented in Fig. In this fashion, the cognitive function of a rat can be quantified as the total number of spots and the total amount of time that the rat stayed still, which will be seen in the last two rows of Table 1.
For the sake of discussion, a red spot, as illustrated in Fig. Figure 12 presents a comparison on the food search trajectory between both groups over the course of 1 month. As time went by, a rat in the TBI group accessed more arms, eventually all the baited and non-baited arms, for food. Besides, it had more repeated access to the arms than earlier.
Instead, a rat in the Sham group had access to less arms than in the TBI case, meaning that the rat had a better spatial memory than the TBI counterpart. Each arm of the maze is 70 cm long and 10 cm wide, and was equally divided into 10 pieces for statistical purpose, each of which occupied an area of 70 cm 2. In addition, the central platform of the maze was divided into 9 pieces, each with an area of 70 cm 2 approximately.
Therefore, the maze was divided into a total of 89 pieces. A rat was detected as a dot which was displayed in color according to the number of times that the rat stayed in a piece, as in rainfall statistics.
For illustrative purposes, the food searching pattern of another rat in the TBI group was presented in Fig. Readers may be misled into thinking that the rat kept accessing all the arms, but did not reach the central platform of the maze.
However, the food searching patterns vary among individuals, and this observation cannot apply to others in the TBI group. Larger allocations are still possible, but only as multiples of the minimum size. It is noted that the bitmap may be kept in the memory manager separate from the heap.
A separate architecture might be advantageous in the context of managing virtual memory. The steps are implemented in software within the memory manager When asked to allocate a block of a given size, the memory manager scans the bitmap to locate a free block of appropriate size step Suppose, for example, the program wanted a block of the size comparable to one of the blocks A, B, C, and D. The memory manager allocates the free block A step The memory manager then updates the corresponding bit value in the bitmap step in FIG.
Accordingly, the memory manager changes the bit entry from free i. An advantage of this memory management system is that free block searching can be conducted entirely within the hierarchical bitmap. Unlike prior art free list systems, there is no need to access the memory heap and follow pointers through the heap to determine what blocks are free.
One significant advantage of the hierarchical bitmap-based memory Manager is that the coalescing process is automatic without any processing cost. Suppose the memory heap has an array of allocated and unallocated blocks.
The allocation states are written in the blocks to assist in describing the example. Included in the blocks is a block , which is currently allocated. It is noted that a memory block can be freed by giving the memory manager any arbitrary pointer into the middle of the block This is unlike prior art memory management systems, which require a pointer to the start of the block in order to change the header and deallocate the block.
With the hierarchical bitmap, the memory blocks themselves do not use headers or trailers to maintain pointers, size information, allocation information, and so forth. When two or more neighboring blocks are marked as free, they are automatically and immediately available for allocation as a single piece of memory. That is, adjoining free blocks of memory inherently coalesce without traditional coalescing processes. In this example, prior to deallocation of block FIG.
Unlike prior art bitmap memory managers, the hierarchical bitmap memory manager enables reallocation of blocks, which involves enlarging or reducing the size of a currently allocated block. These steps are implemented in software within the memory manager At step , the memory manager receives a request for reallocation and determines whether the request is to enlarge or reduce the present block allocation.
Assuming the request is to reduce the size i. The memory manager might already know the present set of blocks allocated to the requester, or alternatively, the reallocation request might contain a pointer to one block within the present allocation. The associated portion of the bitmap is illustrated beside the heap portion Bitmap entries - correspond respectively to the six blocks - At step in FIG. With reference to FIG. With reference again to FIG. At step , the memory manager scans the bitmap for the last block of the memory heap that is presently allocated to the requestor.
Entries - correspond respectively to the six blocks - The memory manager informs the requester that enlargement is not possible at this time. The memory manager determines whether more space is needed to fulfill the reallocation request step in FIG.
If so, the memory manager looks at the next block in the heap step The process continues block by block until either the reallocation request fails, or is fulfilled. Alternatively, the memory manager might make one initial pass to find enough consecutive free space, and a second separate pass to mark the blocks as in use. As this example illustrates, another advantage of the hierarchical bitmap-based memory manager is that the size of an allocated block can be changed enlarged or reduced by modifying only the hierarchical bitmap.
There is no requirement to perform data copies or even to access the memory block itself. In some cases, the application program 92 or the operating system 90 or other program modules 94 may request a specific portion of memory, starting at a specified address.
The hierarchical bitmap scheme is extremely efficient at reserving blocks for specific use, especially in comparison to most prior art techniques and particularly, the free-list approach. To perform the various processes described above, the memory manager does not access the memory blocks themselves, but only the hierarchical bitmap.
The data itself is in fact of no use to the memory manager, and only would result in cache pollution. Conversely, any header information is of no use to the application, and again would result in cache pollution. The hierarchical bitmap memory manager avoids both types of cache pollution.
Big portions of the bitmap, and sometimes the entire bitmap can be loaded into cache for rapid processing, such as searching and reallocation. Since the memory manager only examines two bits per block in this implementation , the cache holds information about many more memory blocks in the same number of bits, as compared to the free-list memory managers. In the case of a sequential search the entire cache line is now useful because the bits are contiguous in the bitmap.
For comparison, the free-list approach would most likely use one cache line per header while searching. For example, if the processor cache line size were 32 bytes, one cache line worth of the bitmap would describe blocks. The same considerations apply to the data portion of a block. If the application is using many small blocks, and especially when accessing them in sequence, there is now a higher probability that the blocks will all be located in the same cache line.
In general, the cache contains a higher average number of blocks per cache line. Another advantage is that the memory accesses to the hierarchical bitmap itself are fully predictable. This enables optimization of cache execution speed by pre-fetching cache lines ahead of the time they are needed. For comparison, the free-list based approach is totally unpredictable; the next header to be examined depends on the dynamics of allocations, deallocations, and coalescing.
One additional advantage is that the hierarchical bitmap memory manager produces naturally aligned allocations. Because the memory manager employs a hierarchical bitmap, the memory blocks themselves are not equipped with headers and trailers, as is common in conventional free list memory managers.
As a result, the memory blocks are inherently aligned for cache operations. Such a method may be used in a system for managing memory blocks in a memory using a hierarchical bitmap. That hierarchical bitmap has entries for directly corresponding memory blocks.
Also, the individual bitmap entries may contain a multi-bit value that represents an allocation state of the corresponding memory block. The memory management method may include the following steps: loading data at in FIG.
Another advantage of the bitmap approach is that it does not mandate any particular search strategy, as would be the case instead for a free-list based approach e.
Any one of the well-known searching strategies can be employed binary search, quicksort, etc. One possible strategy is to always start at the beginning of the heap. Additional information can be maintained to optimize any particular search strategy, for example the use of one or more first-free hints for sequential search.
Another possible strategy could be to search different portions of the bitmap in parallel, either in a shared memory multiprocessor or with the help of specialized hardware. Finally, an implementation might sacrifice some of the cache-friendliness and actually use the data portion of a free block to store any one of many possible auxiliary data structures.
Rather, the specific features and steps are disclosed as preferred forms of implementing the claimed invention. What is claimed is: 1. A memory management system for multiple memory blocks, the memory management system comprising:. A computer-readable medium storing an operating system comprising a memory management system as recited in claim 2. An operating system embodied on a computer-readable medium, the operating system comprising.
TABLE 1. Easily coalesced, sub-allocating, hierarchical, multi-bit bitmap-based memory manager. USB1 en. Method of providing stack memory in an operating system with stack memory constraints.
System and method for allocating storage space using bit-parallel search of bitmap. Method and apparatus for multi-part processing of program code by a single processor. Method and system for selective memory coalescing across memory heap boundaries. System and method for utilizing a hierarchical bitmap structure for locating a set of contiguous ordered search items having a common attribute.
Bitmap manager, method of allocating a bitmap memory, method of generating an acknowledgement between network entities, and network entity implementing the same. System and method for providing client-directed staging to improve non-sequential access performance in a caching disk storage system. EPA2 en. Multimedia system framework having layer consolidating access to multiple media devices.
Media system having synchronization with preemptive prioritization of synchronization order. Method and apparatus for managing a memory for storing potentially configurable entries in a list. USB2 en. Method and system for utilizing a hierarchical bitmap structure to provide a fast and reliable mechanism to represent large deleted data sets in relational databases.
DEA1 en. Device for managing data buffers in a memory space divided into a plurality of memory elements. Methods and systems for managing bandwidth usage among a plurality of client devices. Systems, devices and methods using solid state devices as a caching medium with adaptive striping and mirroring regions.
Systems, devices and methods using a solid state device as a caching medium with a hashing algorithm to maintain sibling proximity. Systems, devices and methods using a solid state device as a caching medium with a write cache flushing algorithm.
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